Impact of the hierarchical medical system on the perceived quality of primary care in China: a quasi-experimental study
Bibliographic record
Abstract
Abstract Background Although the implementation of a hierarchical medical system (HMS) has been shown to improve the allocation of medical resources and patient health-seeking behaviour, its role in patient’s perceived quality of primary care remains unexplored. This study aimed to assess the impact of HMS implementation on rural and urban residents’ perceived quality of primary care. Methods Data were obtained from the China Family Panel Study for 2012, 2014, 2016, and 2018. A total of 40,011 rural and 22,482 urban residents were included in the research participants for analysis. This study adopted a quasi-natural experimental design, and the multiple-period difference-in-differences method was used to capture changes in patient’s perceived quality of primary care before and after the introduction of HMS. Results We found that HMS implementation declined the perceived quality of primary care by an average of 18% among rural residents (OR: 0.82, 95% CI 0.68–0.99), while there was no significant change among urban residents (OR: 1.13, 95% CI 0.87–1.46). There was a 24% reduction in the perceived quality of primary care (OR: 0.76, 95% CI 0.61–0.96) one year after HMS among rural residents, and there was no statistically significant difference two years after HMS. After HMS implementation, the level of perceived quality of primary care by rural patients with chronic diseases decreased by 72% (OR: 0.28, 95% CI 0.11–0.78). Conclusions HMS has a limited effect on improving residents’ perceived quality of primary care, especially for those living in rural areas. Policymakers are suggested to establish a quality monitoring system that incorporates patient experience as an essential standard to systematically evaluate the impacts of the HMS, with more efforts being put into helping vulnerable groups such as residents under 60 years old and patients with chronic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".